Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2069
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dc.contributor.authorDagher, Issamen_US
dc.date.accessioned2020-12-23T09:05:44Z-
dc.date.available2020-12-23T09:05:44Z-
dc.date.issued2010-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/2069-
dc.description.abstractIn this paper highly-compacted DCT coefficients (HDCT) are presented. This compactness is achieved by sorting in ascending order the data first, then by applying the Discrete Cosine transform (DCT) to the ordered data. Images are highly correlated. DCT exhibits excellent energy compaction. It will be shown that HDCT has much better energy compactness than the DCT. This has the effect of representing every ordered image with very small number of HDCT coefficients (dimensionality reduction). The compression capabilities of the HDCT are presented. HDCT is also applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to other algorithms.en_US
dc.format.extent4 p.en_US
dc.language.isoengen_US
dc.subjectDiscrete Cosine Transform (DCT)en_US
dc.subjectEnergy Compactnessen_US
dc.subjectDimensionality reductionen_US
dc.subjectFace recognitionen_US
dc.subject.lcshImage compressionen_US
dc.titleHighly - compacted DCT coefficientsen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Computer Engineeringen_US
dc.description.volume4en_US
dc.description.issue3en_US
dc.description.startpage303en_US
dc.description.endpage307en_US
dc.date.catalogued2017-11-10-
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=https://link.springer.com/content/pdf/10.1007%2Fs11760-009-0119-2.pdfen_US
dc.identifier.OlibID174881-
dc.relation.ispartoftextJournal of signal image and video processingen_US
dc.provenance.recordsourceOliben_US
crisitem.author.parentorgFaculty of Engineering-
Appears in Collections:Department of Computer Engineering
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